Predictive Analytics 1 with R – Machine Learning Tools
This course introduces to the basic predictive modeling paradigm: classification and prediction.
Overview
In this course you will be introduced to basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. You will cover two core paradigms that account for most business applications of predictive modeling: classification and prediction. You will also study commonly used machine learning techniques and learn how to combine models to obtain optimal results. This course includes hands-on work with R, a free software environment with statistical computing capabilities.
- Introductory, Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
At the conclusion of this course you will be able to visualize and explore data, provide an assessment basis for predictive models, and choose appropriate performance measures. You will become familiar with common algorithms including k-nearest-neighbor, Naive Bayes, Classification and Regression Trees, as well as ensemble models.
- Visualize and explore data to better understand relationships among variables
- Organize the predictive modeling task and data flow
- Develop machine learning models with the KNN, Naive Bayes and CART algorithms using R
- Assess the performance of these models with holdout data
- Apply predictive models to generate predictions for new data
- Use various R packages to implement the models in the course
Who Should Take This Course
Marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters. This course is especially useful if you want to understand what predictive modeling might do for your organization, undertake pilots with minimum setup costs, manage predictive modeling projects, or work with consultants or technical experts involved with ongoing predictive modeling deployments.
Our Instructors
Course Syllabus
Week 1
Preparation
- What is supervised learning
- Data partitioning and holdout samples
- Choosing variables (features)
- Handling missing data
- Visualization and exploration
Week 2
Classification and Prediction
- Assessing classification models
- Confusion matrix
- Misclassification costs
- Lift
- Assessing prediction models
- Common metrics
- K-Nearest-Neighbors (KNN)
- Measuring distance
- Choosing k
- Generating classifications and predictions
Week 3
Bayesian Classifiers; CART
- Full Bayes classifier
- Naive Bayes classifier
- Classification and Regression Trees (CART)
- Growing the tree
- Avoiding overfit – pruning
- Using trees for classifications and predictions
Week 4
Ensembles
- Combine multiple algorithms
- Improve results
Prerequisites
You should be familiar with R.
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Predictive Analytics 1 with R – Machine Learning Tools
Additional Information
Time Requirements
15
Homework
Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and end of course data modeling project. Note: There will be a mid-week discussion exercise in the first week of the course.
In addition to assigned readings, this course also has supplemental video lectures, and an end of course data modeling project.
Course Text
The recommended text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, by Shmueli, Patel, Yahav, Bruce and Lichtendahl. This same text is also used in the follow on courses: “Predictive Analytics 2 – Neural Nets and Regression – with R” and “Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules – with R”
Software
This is a hands-on course, and participants will apply data mining algorithms to real data. The course will use R, a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.
Supplemental Information
Literacy, Accessibility, and Dyslexia
At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:
Chrome
- Color Enhancer (for colorblindness)
- HelperBird (for colorblindness, dyslexia, and reading difficulties)
Firefox
- Mobile Dyslexic
- Color Vision Simulation (native accessibility feature)
- Other native accessibility features instructions
Safari
- Navidys (for colorblindness, dyslexia, and reading difficulties)
- HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)
Take a 10-question quiz on analytics: Test Yourself
Whatch our preview of this course:
Watch this video by Dr. Shmueli on “Data Mining in a Nutshell”.
Register For This Course
Predictive Analytics 1 with R – Machine Learning Tools